Hierarchical Clustering (HC) technique is demonstratively applied to analyze the distribution and classification of essential-oil components in oil and dense (subcritical/supercritical) CO2 phases. For this purpose, relative-equilibrium-distribution data obtained for the 24 characteristic components of origanum-oil (Origanum Munituflorum) at 35, 45, 55 ℃ and 20-110 atm pressure range are used. With 24 components and 25 different pressure levels at three different temperatures, the total number of data points amounts to 600, which is large compared to other similar works, making the task of drawing of conclusions by visual inspection quite tedious. As demonstrated in this work, the use of HC technique facilitates the classification of the distribution of essential-oil components. HC-based classification analysis helps to reveal that the distributions of monoterpenes are the most sensitive to changes in temperature and pressure, and they are more soluble in CO2 especially in the supercritical region. At 35 ℃, at higher pressures, due to high solvent density/power, almost all components show similar distributions in the CO2 and oil phases, indicating the loss of fractionation potential. Deterpenation by CO2 is more favorable at higher temperatures. Cophnetic correlation shows the significance level of data clustering. HC analysis proved to be a useful tool in classification of the components and in determination of component clusters in the dense-gas and liquid phases.